



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase							:       Union[str, Any]          =       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       str          =       [
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       str          =       [
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					__lowerCAmelCase							:       str          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
